Comparison study of computational parameter values between LRN and NARX in identifying nonlinear systems

• Main Authors: Nordin F.H., Nagi F.H., Zainul Abidin A.A.
• Other Authors: 25930510500
• Format: Article
• Published: 2023
• Subjects: Layer recurrent network; Nonlinear autoregressive with exogenous inputs; Nonlinear system identification; Recurrent neural network; Complex networks; Identification (control systems); Network layers; Polynomials; Recurrent neural networks; Activation functions; Computational parameters; Correlation coefficient; Investigate and analyze; Non-linear autoregressive with exogenous; Nonlinear autoregressive model with exogenous input (NARX); Polynomial equation; Recurrent networks; Nonlinear systems

To determine the nonlinear autoregressive model with exogenous inputs (NARX) parameter values is not an easy task, even though NARX is reported to successfully identify nonlinear systems. Apart from the activation functions, number of layers, layer size, learning rate, and number of epochs, the numb...